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Updated: May 11, 2026
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Hepatocyte proteome destabilization and novel targets for PFASs unveiled through combined thermal proteome profiling
Zimeng Wu1, Yue Zou1, Kang Yang1
1Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
Abstract:
Identifying protein targets for per- and polyfluoroalkyl substances (PFASs) is essential to understand their toxicity and health risks. However, knowledge about their interacting proteins is limited since reliable identification methods are lacking. We developed an integrated approach combining thermal proteome profiling (TPP) and deep transfer learning (DTL) modeling to efficiently identify cellular targets of PFAS. TPP measured PFAS binding proteins and the affinities by nanospray liquid chromatography tandem mass spectrometry, while DTL models were constructed to predict PFAS-protein affinities using neural network algorithms. TPP results revealed that PFASs uniquely destabilized the proteome of HepG2 cells, unlike the stabilizing effects by other xenobiotics. Key protein targets for three representative PFASs (PFOA, GenX and Novec 649) were identified, which exhibited weak binding affinities (median EC50 ≈ 30 μM). The number of protein targets increased with molecular weights among the three PFASs. The DTL model achieved a higher Pearson correlation coefficient of 0.89, and reduced mean squared errors by 54 % over previous models for drug-protein interactions. Notably, TPP and DTL jointly pinpointed ribosomal proteins as novel targets of GenX, potentially linking it to cell apoptosis through disrupted protein synthesis. Biolayer interferometry validated GenX binding to RPL4 protein, driven by electrostatic interactions and halogen bonds. This integrated approach effectively uncovers novel PFASs targets, advancing insights into their adverse health effects.
Insights
Identifying per- and polyfluoroalkyl substances (PFAS) protein targets is crucial for understanding toxicity. This study introduces an integrated thermal proteome profiling and deep transfer learning approach to efficiently discover novel PFAS-interacting proteins and their health implications.
Area of Science:
- Environmental Chemistry
- Toxicology
- Proteomics
Background:
- Per- and polyfluoroalkyl substances (PFAS) pose health risks, but their molecular targets remain largely unknown.
- Reliable methods for identifying protein interactions with PFAS are limited.
- Understanding PFAS-protein interactions is key to elucidating their toxicity mechanisms.
Purpose of the Study:
- To develop and apply an integrated approach combining thermal proteome profiling (TPP) and deep transfer learning (DTL) for efficient identification of cellular PFAS targets.
- To identify specific protein targets for representative PFAS compounds like PFOA, GenX, and Novec 649.
- To investigate the potential health risks associated with identified PFAS-protein interactions, such as impacts on protein synthesis and cell apoptosis.
Main Methods:
- Utilized thermal proteome profiling (TPP) coupled with nanospray liquid chromatography tandem mass spectrometry to measure PFAS binding proteins and affinities.
- Developed deep transfer learning (DTL) models using neural network algorithms to predict PFAS-protein affinities.
- Employed biolayer interferometry for experimental validation of specific PFAS-protein interactions.
Main Results:
- PFAS uniquely destabilized the proteome of HepG2 cells, contrasting with stabilizing effects of other xenobiotics.
- Identified key protein targets for PFOA, GenX, and Novec 649, showing weak binding affinities (median EC50 ≈ 30 μM).
- The DTL model demonstrated high predictive accuracy (Pearson correlation coefficient = 0.89), outperforming previous models.
- Discovered ribosomal proteins as novel targets for GenX, suggesting a link to apoptosis via disrupted protein synthesis.
- Validated GenX binding to RPL4 protein, driven by electrostatic interactions and halogen bonds.
Conclusions:
- The integrated TPP and DTL approach is effective for uncovering novel PFAS targets.
- Identified protein targets provide new insights into the adverse health effects of PFAS exposure.
- This methodology advances the understanding of PFAS toxicology and risk assessment.
